Instructions to use HReynaud/EchoFlow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HReynaud/EchoFlow with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("HReynaud/EchoFlow", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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# EchoFlow
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license: apache-2.0
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library_name: diffusers
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pipeline_tag: image-to-image
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# EchoFlow
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This repository contains the models described in [EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation](https://huggingface.co/papers/2503.22357).
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Try out the model at https://huggingface.co/spaces/HReynaud/EchoFlow.
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